The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: Workshop on Artificial Intelligence and Machine Learning for Scientific Applications
Authors: Nadav Schneider (Soreq Nuclear Research Center (SNRC), Israel); Matan Rusanovsky (Nuclear Research Center Negev, Israel); Raz Gvishi (Soreq Nuclear Research Center (SNRC), Israel); and Gal Oren (Technion - Israel Institute of Technology)
Abstract: HEDP experiments commonly involve a dynamic wave-front propagating inside a low-density foam. To classify the foams' quality, accurate information is required. For each foam, five images are taken: two 2D images representing the top and bottom surface foam planes and three images of side cross-sections from 3D scannings. An expert has to do the complicated, harsh, and exhausting work of manually classifying the foam's quality through the image set and only then determine whether the foam can be used in experiments or not. In this work, we present a novel state-of-the-art multi-view deep-learning classification model determining the foams' quality classification and thus aids the expert. Our model achieved 86% accuracy on upper and lower surface foam planes and 82% on the entire set, suggesting interesting heuristics to the problem. A significant added value in this work is the ability to regress the foam quality and even explain the decision visually.